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# VARs, SVARs and VECMs Ibrahim Stevens Joint HKIMR/CCBS Workshop

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VARs, SVARs and VECMs Ibrahim Stevens Joint HKIMR/CCBS Workshop
Advanced Modelling for Monetary Policy in the Asia-Pacific Region May 2004

Contents What is a VAR? Advantages and disadvantages of VARs
Number of lags Identification Impulse response functions Variance decomposition SVARs VECMs

What is a VAR? Every stationary univariate process has an autoregressive representation (see Wold decomposition): Useful if the process can be modelled well using few lags, eg must be small relative to xt and be ‘well-behaved’ (eg white noise)

What is a VAR? Let {xt} be a sequence of vectors rather than scalars and you have a VAR! The B’s are now matrices of coefficients and is a vector of constants. (It is useful to work with few lags) Again we wish to be small relative to xt and to be ‘well-behaved’ (eg white noise)

Why use VARs VARs treat all variables in the system as endogenous
(Might) avoid ‘incredible identification’ problems Model the dynamic response to shocks Can aid identification of shocks - including monetary policy shocks Often considered good for short-term forecasting

Problems with using VARs
Identification is a big issue different identification schemes can give very different results ditto different lag lengths Tend to be over-fitted (too many variables and lags)

An Example Of Economic Model
Firms set free prices (f) on the basis of current overall price levels; Government sets administered prices (g) on the basis of current overall price level; The overall price level is a weighted sum of free and administered prices So:

Structural system of interest
Or,

Structural system of interest
Or,

Structural system of interest
Where, Are polynomials in the lag operator L

Big Problem 1 We can’t estimate the structural system directly
Instead of: We estimate the reduced form:

Estimation issue 1 Which estimation method should we choose?
We can use OLS because all variables are the same on the RHS for each equation As long as we use the same number of lags for each equation [If not, more efficient to use SUR] Near-VAR models

Estimation issue 2 Should the variables in the VAR be stationary?
Non-stationary variables might lead to spurious results But Chris Sims argues that, by differencing or detrending, a lot of information is lost And that we are interested in the inter-relationships between variables, not the coefficients themselves

Estimation issue 3 How many lags should we use?
More lags improve the fit … … but reduce the degrees of freedom and increase the likelihood of over-fitting

Overfitting For a stationary process
But this does not imply that you improve the model by increasing the number of lags A big problem is noisy data. By increasing the number of explanatory variables you may be ‘explaining’ noise, not the underlying DGP

Estimation issue 3 Two ways round this problem:
Economic theory might suggest appropriate lag length Use lag length selection criteria: trade-off parsimony against fit Think about the results do you believe them? do they suggest you should seasonally adjust?

Big Problem 2 We can move easily from the structural model to the reduced form But not the other way In this example we estimate 7 parameters but the structural model has 8 Therefore there are an infinite number of ways of moving from reduced form to the unobserved structural model

Big Problem 2 So, 8 unknowns and 7 estimates, hmm
We need to make one of the unknowns known ie we need to set the value of (at least) one coefficient [make an identifying restriction] If we make 1 identifying restriction the system is just identified If we make more than 1, it is over identified: we can then test restrictions

Unrestricted VARs and SVARs
To summarise: We estimate an unrestricted (or reduced-form) VAR By imposing suitable theoretical restrictions we can recover the restricted (or structural) VAR The unrestricted VAR is a statistical description of the data, the SVAR adds some economics

Unrestricted VARs vs SVARs
Unrestricted VAR compatible with a lot of theories: produces good short-term forecast Structural VAR tied to a particular theory: provides a better interpretation of forecast. Long-term forecast is a combination of both Structural VAR is better for a Central Bank??

Identifying restrictions
1. Recursive/Wold chain/Cholesky decomposition. Sims’ lag of data availability 2. Coefficient restriction: eg a12=1 3. Variance restriction: eg Var(1)=3 4. Symmetry restriction : eg a12=a21 5. Long-run restrictions, eg nominal shocks don’t affect real variables.

Example The g equation of, is,

Example If a12 =0 we have, ie the reduced-form error on the equation is the same as the structural error This is the Cholesky decomposition We are assuming a recursive ordering of the transmission of fundamental shocks in the system

Cholesky decomposition
In order to recover the fundamental shocks to the system we restrict the A matrix to be triangular (zeroes above or below the main diagonal) We need to be confident about the ordering of the impact of shocks The frequency of the data becomes a big issue Nonetheless, probably the most common form of identification

Cholesky Decomposition
Take the system: Then:

Monetary Policy “There is little hope that economists can evaluate alternative theories of monetary policy transmission, or obtain quantitative estimates of the impact of monetary policy changes on various sectors in the economy, if there exists no reasonably objective means of determining the direction and size of changes in policy stance” Bernanke and Mihov (QJE, 1998)

Using the Cholesky decomposition
Huge literature on identifying monetary policy shocks Typically use recursive identification schemes Note that if we are interested in just one of the structural shocks we don’t have to identify the whole system Nonetheless, controversies abound eg what is the monetary policy instrument what is the information set of the policy maker

Assessing the stance of MP
Bernanke and Mihov (1998) use an SVAR to determine the stance of monetary policy Essentially they estimate an MCI but try to get round the problem of shock identification They claim they do this by identifying fundamental shocks to policy

Bernanke and Mihov - method
They use a block-recursive identification scheme with two blocks: policy and non-policy They assume that policy has no effect on the non-policy variables in the initial period They identify shocks to the policy variables (FFR, exchange rate, term spread) But do not identify the fundamental shocks to the non-policy part of the system

Bernanke and Mihov – semi-structural VARs
Take the VAR with variables GDP (Y), CPI (P), price of raw materials (Pcm), Federal Funds Rate (FF), total bank reserves (TR) and non-borrowed reserves (NBR) They assume: Orthogonality of structural disturbances Macroeconomic variables do not react to changes in the monetary variables Restrictions on the monetary block

Bernanke and Mihov – semi-structural VAR
For point 3, Bernanke y Mihov assume:

Bernanke and Mihov –semi-structural VAR
To identify the structural disturbances:

Bernanke and Mihov – semi-structural VAR
One still needs to make other restrictions which depend on theory

Blanchard-Quah Alternative way of identifying the VAR is to think about variables interaction in the long run Blanchard and Quah (AER, 1989) advocate an identification scheme where supply shocks have permanent effects on real variables demand shocks only have temporary effects on real variables

Blanchard-Quah As a VMA,
Take the structural VAR we are interested in ( Blanchard-Quah use GDP and unemployment) As a VMA,

Blanchard-Quah The VAR we estimate (reduced form) in VMA form We must impose restrictions on D(L) to obtain A(L) and B. Assume (1-A(L))-1B=C(L)

Blanchard-Quah We can see If we assume that shocks to GDP are demand shocks and shocks to unemployment are supply shocks, we can assume that GDP shocks will not have a permanent effect on GDP, that is C11(L) yt=0 Under this assumption one can recover the demand and supply shocks from the reduced VAR

Using Blanchard-Quah Quah and Vahey (EJ, 1995) use the BQ technique to identify core inflation They define core inflation as that part of inflation that has persistent effects on the price level So core inflation is like a real variable in the BQ paper and transitory inflation like a nominal variable

Faust and Rogers (2000) Investigate:
How delayed is the overshooting of the exchange rate following a nominal shock Test to see how well UIP fares Ask what proportion of the volatility of the exchange rate can be explained by MP shocks Find: delay of overshooting is sensitive to assumptions UIP performs miserably between 2 and 30% of the volatility is explained by MP shocks

Faust and Rogers - Technique
Under identify the VAR arguing: few variables make identification easier but you have omitted variables but you can only be confident about a few restrictions with bigger systems Then run the VARs using other possible restrictions If the impulse responses are similar: fantastic! Otherwise choose the impulse response you think best fits reality

Impulse Response Functions
Perhaps the most useful product of VARs Use the moving average representation of a VAR to show the dynamic response of variables to fundamental shocks Often used to determine the lags of the monetary transmission mechanism, etc

Example of going from AR to MA
Take a VAR with one lag

Impulse response for an AR(1)
The effect on {xt} of a one-period shock is given by the MA representation In this case Here

Variance decomposition
Analyses the relative ‘importance’ of variables More precisely, the proportion over time of the variance of a variable due to each fundamental shocks eg, in the Faust and Rogers paper, they look at what proportion of the volatility of the exchange rate is explained by the various shocks

General principles of identifying VARs
Identify what you are interested in eg if you simply want a forecast, do you need to identify it at all? Think about the economics Test for robustness Compare impulse responses - do you believe them? eg the ‘price puzzle’

Cointegration Recall, a vector of I(n) variables are cointegrated if there is a (non-trivial) linear combination of them which is I(m) where m < n Normally we are thinking about combinations of I(1) variables which are I(0) If they are cointegrated there is an error-correction representation of the variables In economic terms, variables are cointegrated if there is a long-run equilibrium relationship between them

VECMs Vector error correction mechanisms are generalisations of ECMs
zt are the equilibrium relationships

VECMs Like ECMs, VECMs combine short-run (dynamic) information with long-run (static) information They are like a combination of an unrestricted VAR (the dynamic part) and a structural VAR (the long-run is (should be) consistent with theory)

Identification Issues
As with VARs you estimate it with no current dependent variables on the RHS But theory might suggest that the structural model should include them Finding the cointegrating vectors (and even knowing how many there are) is a fragile business If there are n variables in the system there are (n-1) possible cointegrating vectors

Johansen You are testing the rank of the matrix A-1C in the equation below If it has less than full rank there is at least one cointegrating vector

Johansen with 2 variables
If there is an equilibrium relationship between x and z The are the ‘loadings’ and gives the equilibrium relationship

Estimating a VECM: technicalities
Lag length of VECM: - Gaussian residuals Worth considering exogenous short-run variables (eg dummies, other economic variables) Inclusion of deterministic components (constant, trend) - Johansen (1992) suggests: - Begin with estimation of most restrictive model (no constant, no trends) and estimate less and less restrictive variants until reach least restrictive (constant, trends) - Compare trace stats to their critical level -Stop the first time null is not rejected

Estimating VECM: technicalities
Identification of cointegrating vectors: - Johansen test tells you the number of cointegrating vectors; but it does NOT tell you if these are unique - If one cointegrating vector, then the vector is identified - If more than one cointegrating vector, we must impose restrictions

Estimating VECM: technicalities
Imposing restrictions is ‘easy’. These can be imposed as follows: There are r cointegrating vectors (found in matrix ), then H : =(H11, H22,…,Hrr) where Hi i=1,…,r are matrices representing the linear economic relationships to be tested and i i=1,…,r are vectors with parameters to be estimated (for each of the cointegrating relationships)

Estimating VECM: technicalities
If we choose a matrix Ri orthogonal to Hi (Ri`Hi=0) then testing a restriction on the first vector is equivalent to: rank(R1`( 1,  2,…,  r)) = rank(R1`(H11, H22,…,Hrr)) = r-1 ie identification is achieved if applying the restrictions of the first vector to the other r-1 vectors results in a matrix of rank r-1.

Gonzalo and Ng (2001) The ‘loadings’ identify the effects of the disequilibrium terms on the endogenous variables Show that the orthogonal complement of loadings and cointegrating vectors: can be used to define Permanent and Transitory shocks Apply Choletsky, can do variance decomposition

IDENTIFICATION AY(t)=BY(t-1)+CE(t) where E(t) is white noise with Mean 0 and variance-covariance I Y(t)=A -1BY(t-1)+ A -1CE(t) is the atheoretical VAR C is diagonal Orthogonal Shocks (N(N-1))/2 A is triangular Causal Ordering (N(N-1))/2 A -1B=P Long run (I-A -1B)-1A -1C=Q Blanchard and Quah A -1B=I+R Johansen

Textbooks Hamilton, James D, (1994) ‘Time Series Analysis’, Princeton University Press Enders, Walter, (1995) ‘Applied econometric time series’, Wiley Harris, Richard (1995), ‘Cointegration Analysis in Econometric Modelling’, Harvester Wheatsheaf Identifying Monetary Policy Shocks Christiano, Lawrence J.; Eichenbaum, Martin; Evans, Charles L, (1999) ‘Monetary Policy Shocks: What Have We Learned and to What End?’ in Taylor, John B.; Woodford, Michael, eds. Handbook of Macroeconomics. Volume 1A. Handbooks in Economics, vol. 15, North Holland Leeper, Eric M.; Sims, Christopher A.; Zha, Tao, (1996) ‘What Does Monetary Policy Do?’, Brookings Papers on Economic Activity; 0(2), 1996, pages 1 63 Bernanke, Ben S.; Mihov, Ilian (1998) ‘Measuring Monetary Policy’, Quarterly Journal of Economics; 113(3), August 1998, pages Blanchard Quah Blanchard, Olivier Jean; Quah, Danny. (1989) ‘The Dynamic Effects of Aggregate Demand and Supply Disturbances’, American Economic Review; 79(4), September 1989, pages Quah, Danny; Vahey, Shaun P (1995) ‘Measuring Core Inflation’, Economic Journal; 105(432), September 1995, pages Others Faust, John and John Rogers, (2003?), ‘Monetary Policy’s Role in Exchange Rate Behaviour’, Journal of Monetary Economics, forthcoming

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